2022-10-23 added stock selection model

fundamental_run_model.py

@@ -0,0 +1,101 @@
+import warnings
+warnings.filterwarnings("ignore")
+
+import pandas as pd
+import numpy as np
+import time
+import traceback
+import sys
+sys.path.append('code')
+import ml_model
+
+
+
+
+if __name__ == '__main__':
+    import argparse
+    parser = argparse.ArgumentParser()
+
+    #sector name
+    parser.add_argument('-sector_name','--sector_name_input', type=str,  required=True,help='sector name: i.e. sector10')
+
+    # file name
+    parser.add_argument('-fundamental','--fundamental_input', type=str,  required=True,help='inputfile name for fundamental table')
+    parser.add_argument('-sector','--sector_input', type=str,  required=True,help='inputfile name for individual sector')
+
+    # rolling window variables
+    parser.add_argument("-first_trade_index", default=20, type=int)
+    parser.add_argument("-testing_window", default=4, type=int)
+
+    # column name
+    parser.add_argument("-label_column", default='y_return', type=str)
+    parser.add_argument("-date_column", default='date', type=str)
+    parser.add_argument("-tic_column", default='tic', type=str)
+    parser.add_argument("-no_feature_column_names", default = ['gvkey', 'tic', 'datadate', 'rdq', 'datadate', 'fyearq', 'fqtr',
+       'conm', 'datacqtr', 'datafqtr', 'gsector','y_return'], type=list,help='column names that are not fundamental features')
+
+
+
+    args = parser.parse_args()
+    #load fundamental table
+    inputfile_fundamental = args.fundamental_input
+
+    fundamental_total=pd.read_csv(inputfile_fundamental)
+   # fundamental_total=fundamental_total[fundamental_total['tradedate'] < 20170901]
+    #get all unique quarterly date
+    # load sector data
+    inputfile_sector = args.sector_input
+
+    sector_data=pd.read_excel(inputfile_sector)
+    unique_datetime = sorted(sector_data.date.unique())
+
+
+    #get sector unique ticker
+    unique_ticker=sorted(sector_data[args.tic_column].unique())
+
+    #set rolling window
+    # train: 4 years = 16 quarters
+    # test: 1 year = 4 quarters
+    # so first trade date = #20 quarter
+    #first trade date is 1995-06-01
+    first_trade_date_index=args.first_trade_index
+
+    #testing window
+    testing_windows = args.testing_window
+
+    #get all backtesting period trade dates
+    trade_date=unique_datetime[first_trade_date_index:]
+    #variable column name
+    label_column = args.label_column
+    date_column = args.date_column
+    tic_column = args.tic_column
+
+    # features column: different base on sectors
+    no_feature_column_names = args.no_feature_column_names
+    features_column = [x for x in sector_data.columns.values if (x not in no_feature_column_names) and (np.issubdtype(sector_data[x].dtype, np.number) and(not np.any(np.isnan(sector_data[x]))))]
+
+    #sector name
+    sector_name = args.sector_name_input
+
+    try:
+        start = time.time()
+        model_result=ml_model.run_4model(sector_data,
+                                            features_column, 
+                                            label_column, 
+                                            date_column,
+                                            tic_column,
+                                            unique_ticker, 
+                                            unique_datetime, 
+                                            trade_date,
+                                            first_trade_date_index,
+                                            testing_windows)
+        end = time.time()
+        print('Time Spent: ',(end-start)/60,' minutes')
+        ml_model.save_model_result(model_result,sector_name)
+
+    except e:
+        print(e)
+
+
+
+# python3 fundamental_run_model.py -sector_name sector10 -fundamental Data/fundamental_final_table.xlsx -sector Data/1-focasting_data/sector10_clean.xlsx